{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:55:38Z","timestamp":1782402938907,"version":"3.54.5"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T00:00:00Z","timestamp":1624406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Signal identification is of great interest for various applications such as spectrum sharing and interference management. A typical signal identification system can be divided into two steps. A feature vector is first extracted from the received signal, then a decision is made by a classification algorithm according to its observed values. Some existing techniques show good performance but they are either sensitive to noise level or have high computational complexity. In this paper, a machine learning algorithm is proposed for the identification of vehicular communication signals. The feature vector is made up of Instantaneous Frequency (IF) resulting from time\u2013frequency (TF) analysis. Its dimension is then reduced using the Singular Value Decomposition (SVD) technique, before being fed into a Random Forest classifier. Simulation results show the relevance and the low complexity of IF features compared to existing cyclostationarity-based ones. Furthermore, we found that the same accuracy can be maintained regardless of the noise level. The proposed framework thus provides a more accurate, robust and less complex V2X signal identification system.<\/jats:p>","DOI":"10.3390\/s21134286","type":"journal-article","created":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T11:28:41Z","timestamp":1624447721000},"page":"4286","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["V2X Wireless Technology Identification Using Time\u2013Frequency Analysis and Random Forest Classifier"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6707-0044","authenticated-orcid":false,"given":"Camelia","family":"Skiribou","sequence":"first","affiliation":[{"name":"COSYS-LEOST, University Gustave Eiffel, IFSTTAR, F-59650 Villeneuve d\u2019Ascq, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0408-3486","authenticated-orcid":false,"given":"Fouzia","family":"Elbahhar","sequence":"additional","affiliation":[{"name":"COSYS-LEOST, University Gustave Eiffel, IFSTTAR, F-59650 Villeneuve d\u2019Ascq, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kiela, K., Barzdenas, V., Jurgo, M., Macaitis, V., Rafanavicius, J., Vasjanov, A., Kladovscikov, L., and Navickas, R. (2020). Review of V2X\u2013IoT Standards and Frameworks for ITS Applications. Appl. Sci., 10.","DOI":"10.3390\/app10124314"},{"key":"ref_2","unstructured":"ETSI (2019). ITS-G5 Access layer specification for Intelligent Transport Systems operating in the 5 GHz frequency band. EN 302 663-V1.3.1-Intelligent Transport Systems (ITS), ETSI. Technical Report."},{"key":"ref_3","unstructured":"ETSI (2017). Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2 (3GPP TS 36.300 version 14.2.0 Release 14). TS 136 300-V14.2.0-LTE, ETSI. Technical Report."},{"key":"ref_4","unstructured":"ETSI (2020). 5G; Overall description of Radio Access Network (RAN) aspects for Vehicle-to-everything (V2X) based on LTE and NR (3GPP TR 37.985 version 16.0.0 Release 16). TR 137 985-V16.0.0-LTE, ETSI. Technical Report."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"140145","DOI":"10.1109\/ACCESS.2020.3012788","article-title":"Survey of Spectrum Regulation for Intelligent Transportation Systems","volume":"8","author":"Choi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1049\/iet-com:20050176","article-title":"Survey of automatic modulation classification techniques: Classical approaches and new trends","volume":"1","author":"Dobre","year":"2007","journal-title":"IET Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/S1005-8885(09)60471-4","article-title":"Digital modulation recognition based on instantaneous information","volume":"17","author":"Hu","year":"2010","journal-title":"J. China Univ. Posts Telecommun."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Moser, E., Moran, M.K., Hillen, E., Li, D., and Wu, Z. (2015, January 15\u201319). Automatic modulation classification via instantaneous features. Proceedings of the IEEE National Aerospace Electronics Conference, NAECON, Dayton, OH, USA.","DOI":"10.1109\/NAECON.2015.7443070"},{"key":"ref_9","unstructured":"Le Martret, C.J., and Boitea, D.M. (1997, January 3\u20135). Modulation classification by means of different orders statistical moments. Proceedings of the IEEE Military Communications Conference MILCOM, Monterey, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/26.837045","article-title":"Hierarchical digital modulation classification using cumulants","volume":"48","author":"Swami","year":"2000","journal-title":"IEEE Trans. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1109\/TWC.2013.021213.111888","article-title":"Second-order cyclostationarity of BT-SCLD signals: Theoretical developments and applications to signal classification and blind parameter estimation","volume":"12","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Karami, E., Dobre, O.A., and Adnani, N. (2015, January 11\u201314). Identification of GSM and LTE signals using their second-order cyclostationarity. Proceedings of the Conference Record-IEEE Instrumentation and Measurement Technology Conference, Pisa, Italy.","DOI":"10.1109\/I2MTC.2015.7151426"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/JSTSP.2011.2174773","article-title":"Second-order cyclostationarity of mobile WiMAX and LTE OFDM signals and application to spectrum awareness in cognitive radio systems","volume":"6","author":"Dobre","year":"2012","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Al-Nuaimi, D.H., Hashim, I.A., Zainal Abidin, I.S., Salman, L.B., and Mat Isa, N.A. (2019). Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification\u2014A Review. Electronics, 8.","DOI":"10.3390\/electronics8121407"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shi, Y., Davaslioglu, K., Sagduyu, Y.E., Headley, W.C., Fowler, M., and Green, G. (2019, January 11\u201314). Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments. Proceedings of the 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Newark, NJ, USA.","DOI":"10.1109\/DySPAN.2019.8935684"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bitar, N., Muhammad, S., and Refai, H.H. (2017, January 8\u201313). Wireless technology identification using deep convolutional neural networks. Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292183"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, X., Dong, F., Zhang, S., and Guo, W. (2019). A Survey on Deep Learning Techniques in Wireless Signal Recognition. Wirel. Commun. Mob. Comput.","DOI":"10.1155\/2019\/5629572"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, L.X., and Ren, Y.J. (2009, January 8\u20139). Recognition of digital modulation signals based on high order cumulants and support vector machines. Proceedings of the 2009 Second ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM 2009, Sanya, China.","DOI":"10.1109\/CCCM.2009.5267733"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1007\/978-3-319-09339-0_53","article-title":"A signal modulation type recognition method based on kernel PCA and random forest in cognitive network","volume":"Volume 8589","author":"Wang","year":"2014","journal-title":"Intelligent Computing Methodologies"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"138890","DOI":"10.1109\/ACCESS.2019.2942368","article-title":"Multi-dimensional wireless signal identification based on support vector machines","volume":"7","author":"Tekbiyik","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","unstructured":"Tekb\u0131y\u0131k, K., Akbunar, O., Ekti, A.R., G\u00f6r\u00e7in, A., Kurt, G.K., and Qaraqe, K.A. (2020). Spectrum Sensing and Signal Identification with Deep Learning based on Spectral Correlation Function. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Math. Phys. Eng. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cmpb.2011.11.005","article-title":"Automated sleep stage identification system based on time\u2013frequency analysis of a single EEG channel and random forest classifier","volume":"108","author":"Fraiwan","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Anwar, W., Franchi, N., and Fettweis, G. (2019, January 22\u201325). Physical layer evaluation of V2X communications technologies: 5G NR-V2X, LTE-V2X, IEEE 802.11bd, and IEEE 802.11p. Proceedings of the IEEE Vehicular Technology Conference, Honolulu, HI, USA.","DOI":"10.1109\/VTCFall.2019.8891313"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sattiraju, R., Wang, D., Weinand, A., and Schotten, H.D. (2020, January 25\u201328). Link Level Performance Comparison of C-V2X and ITS-G5 for Vehicular Channel Models. Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium.","DOI":"10.1109\/VTC2020-Spring48590.2020.9129366"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bazzi, A., Cecchini, G., Menarini, M., Masini, B.M., and Zanella, A. (2019). Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era. Future Internet, 11.","DOI":"10.3390\/fi11060122"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/8849610","article-title":"Network Performance Test and Analysis of LTE-V2X in Industrial Park Scenario","volume":"2020","author":"Fan","year":"2020","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mannoni, V., Berg, V., Sesia, S., and Perraud, E. (May, January 28). A Comparison of the V2X Communication Systems: ITS-G5 and C-V2X. Proceedings of the IEEE Vehicular Technology Conference (VTC) Spring 2019, Kuala Lumpur, Malaysia.","DOI":"10.1109\/VTCSpring.2019.8746562"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bagheri, H., Noor-A-Rahim, M., Liu, Z., Lee, H., Pesch, D., Moessner, K., and Xiao, P. (2020). 5G NR-V2X: Towards Connected and Cooperative Autonomous Driving. arXiv.","DOI":"10.1109\/MCOMSTD.001.2000069"},{"key":"ref_30","first-page":"100340","article-title":"DMRS-based channel estimation for railway communications in tunnel environments","volume":"29","author":"Skiribou","year":"2021","journal-title":"Veh. Commun."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jouny, I. (2014, January 19\u201323). Target recognition using scattering features extracted with EMD. Proceedings of the 2014 IEEE Radar Conference, Cincinnati, OH, USA.","DOI":"10.1109\/RADAR.2014.6875569"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2008\/647135","article-title":"A fault diagnosis approach for gears based on IMF AR model and SVM","volume":"2008","author":"Cheng","year":"2008","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1007\/978-3-319-73712-6_12","article-title":"Dimensionality reduction using PCA and SVD in big data: A comparative case study","volume":"Volume 220","author":"Tanwar","year":"2018","journal-title":"International Conference on Future Internet Technologies and Trends"},{"key":"ref_34","unstructured":"Louppe, G. (2014). Understanding Random Forests: From Theory to Practice. [Ph.D. Thesis, University of Li\u00e8ge]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","unstructured":"Breiman, L. (1994). Bagging Predictors, Springer. Technical Report."},{"key":"ref_37","unstructured":"(2013). MATLAB, The MathWorks Inc.. version 8.1.0.604 (R2013a)."},{"key":"ref_38","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_39","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.physa.2014.01.020","article-title":"On the computational complexity of the empirical mode decomposition algorithm","volume":"400","author":"Wang","year":"2014","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1007\/s10444-014-9345-4","article-title":"An algorithm for fast hilbert transform of real functions","volume":"40","author":"Bilato","year":"2014","journal-title":"Adv. Comput. Math."},{"key":"ref_42","unstructured":"Li, X., Wang, S., and Cai, Y. (2019). Tutorial: Complexity analysis of Singular Value Decomposition and its variants. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4286\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:21:51Z","timestamp":1760163711000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4286"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,23]]},"references-count":42,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134286"],"URL":"https:\/\/doi.org\/10.3390\/s21134286","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,23]]}}}